The History of the Quantitative Methods in Finance Conference Series. 1992-2007

This report charts the history of the Quantitative Methods in Finance (QMF) conference from its beginning in 1993 to the 15th conference in 2007. It lists alphabetically the 1037 speakers who presented at all 15 conferences and the titles of their papers.

Yield Curve Arbitrage in EUR Swap Rates: A Hybrid Neural Network Approach

In this thesis, I analyze the out-of-sample trading performance of a yield curve arbitrage strategy on EUR swap rates. For the implementation of the strategy, I introduce a novel hybrid neural network approach which uses the factors of an affine term structure model as inputs. I compare the results to the performance of a benchmark strategy that is based on the traditional two-factor Vasicek term structure model. The results imply that with reasonable transaction costs, the neural network model produces significant multifactor alpha, positively skewed returns with high kurtosis and a higher Sharpe ratio and higher absolute cumulative performance compared to the Vasicek model. However, the neural network strategy also has exposure to systematic risk factors and tail risk

The Application of Neural Networks to the Pricing of Credit Derivatives

The present paper deals with a new approach to the pricing of credit derivatives, which are innovative financial instruments able to immunize a securities portfolio from the default risk of the issuers, using neural networks. After an essential analysis of the most important topics inherent to these nonlinear statistical instruments, particular emphasis, due to their diffusion, has been put on the characters of Credit Default Swaps and on the particularities of the structural and reduced form approaches proposed for their analysis. In the final part of the paper the effectiveness of neural networks in approximating the evaluation of credit derivatives and in improving the timing in the default prevision is illustrated.

Deep xVA solver -- A neural network based counterparty credit risk management framework

In this paper, we present a novel computational framework for portfolio-wide risk management problems, where the presence of a potentially large number of risk factors makes traditional numerical techniques ineffective. The new method utilises a coupled system of BSDEs for the valuation adjustments (xVA) and solves these by a recursive application of a neural network based BSDE solver. This not only makes the computation of xVA for high-dimensional problems feasible, but also produces hedge ratios and dynamic risk measures for xVA, and allows simulations of the collateral account.Comment: 33 pages. Several experiments adde

A Comprehensive Survey on Enterprise Financial Risk Analysis: Problems, Methods, Spotlights and Applications

Enterprise financial risk analysis aims at predicting the enterprises' future financial risk.Due to the wide application, enterprise financial risk analysis has always been a core research issue in finance. Although there are already some valuable and impressive surveys on risk management, these surveys introduce approaches in a relatively isolated way and lack the recent advances in enterprise financial risk analysis. Due to the rapid expansion of the enterprise financial risk analysis, especially from the computer science and big data perspective, it is both necessary and challenging to comprehensively review the relevant studies. This survey attempts to connect and systematize the existing enterprise financial risk researches, as well as to summarize and interpret the mechanisms and the strategies of enterprise financial risk analysis in a comprehensive way, which may help readers have a better understanding of the current research status and ideas. This paper provides a systematic literature review of over 300 articles published on enterprise risk analysis modelling over a 50-year period, 1968 to 2022. We first introduce the formal definition of enterprise risk as well as the related concepts. Then, we categorized the representative works in terms of risk type and summarized the three aspects of risk analysis. Finally, we compared the analysis methods used to model the enterprise financial risk. Our goal is to clarify current cutting-edge research and its possible future directions to model enterprise risk, aiming to fully understand the mechanisms of enterprise risk communication and influence and its application on corporate governance, financial institution and government regulation

Credit Pit Detection in Subordinate Securities: A French Perspective

The purpose of this research is to prepare a predictive model for identifying credit crisis using an artificial neural network. The paper also aims to find out the driver and driven relationship between various financial instruments like CDS, FRA, IRS, and the Volatility index (VCAC) and government securities for France. The model, thus, is directed towards finding a threshold for credit pit events and linking various events corresponding to that dates where the threshold is breached to validate the accuracy and usefulness of the model. From the research, it is found that for France, the CDS-FRA-VCAC model derives the threshold for VCAC to indicate the probability of credit crisis or financial market crash. It is also found that sovereign bonds have a huge impact on France economy including various derivatives. This is probably why the Eurozone debt crisis impacted France much more than the 2008 financial crash